import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCamelCase__  =					logging.getLogger(__name__)


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Optional[Any]:




  '''simple docstring'''

  return (preds == labels).mean()




@dataclass
class 					_UpperCAmelCase			:



 '''simple docstring'''




 __A			       =					field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
 __A			       =					field(
     default=lowerCAmelCase,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
 __A			       =					field(
     default=lowerCAmelCase,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
 __A			       =					field(
     default=lowerCAmelCase,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},			)




@dataclass
class 					_UpperCAmelCase			:



 '''simple docstring'''




 __A			       =					field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()  )}  )
 __A			       =					field(metadata={'''help''': '''Should contain the data files for the task.'''}  )
 __A			       =					field(
     default=128,			metadata={
         '''help''': (
             '''The maximum total input sequence length after tokenization. Sequences longer '''
             '''than this will be truncated, sequences shorter will be padded.'''
         )
     },			)
 __A			       =					field(
     default=lowerCAmelCase,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )


def 				lowerCAmelCase__       (			)      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir )
      and os.listdir(training_args.output_dir )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
        " --overwrite_output_dir to overcome." )

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,     datefmt="%m/%d/%Y %H:%M:%S"    ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,     )
  logger.warning(
      "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"    ,     training_args.local_rank    ,     training_args.device    ,     training_args.n_gpu    ,     bool(training_args.local_rank != -1 )    ,     training_args.fpaa    ,     )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info("Training/evaluation parameters %s"    ,     a__ )

  # Set seed
  set_seed(training_args.seed )

  try:
    _UpperCamelCase           =    processors[data_args.task_name]()
    _UpperCamelCase           =    processor.get_labels()
    _UpperCamelCase           =    len(a__ )
  except KeyError:
    raise ValueError("Task not found: %s" % (data_args.task_name) )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _UpperCamelCase           =    AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,     num_labels=a__    ,     finetuning_task=data_args.task_name    ,     cache_dir=model_args.cache_dir    ,     )
  _UpperCamelCase           =    AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,     cache_dir=model_args.cache_dir    ,     )
  _UpperCamelCase           =    AutoModelForMultipleChoice.from_pretrained(
      model_args.model_name_or_path    ,     from_tf=bool(".ckpt" in model_args.model_name_or_path )    ,     config=a__    ,     cache_dir=model_args.cache_dir    ,     )

  # Get datasets
  _UpperCamelCase           =    (
      MultipleChoiceDataset(
          data_dir=data_args.data_dir    ,     tokenizer=a__    ,     task=data_args.task_name    ,     max_seq_length=data_args.max_seq_length    ,     overwrite_cache=data_args.overwrite_cache    ,     mode=Split.train    ,     )
      if training_args.do_train
      else None
  )
  _UpperCamelCase           =    (
      MultipleChoiceDataset(
          data_dir=data_args.data_dir    ,     tokenizer=a__    ,     task=data_args.task_name    ,     max_seq_length=data_args.max_seq_length    ,     overwrite_cache=data_args.overwrite_cache    ,     mode=Split.dev    ,     )
      if training_args.do_eval
      else None
  )

  def compute_metrics(a__ ) -> Dict:
    _UpperCamelCase           =    np.argmax(p.predictions    ,     axis=1 )
    return {"acc": simple_accuracy(a__    ,     p.label_ids )}

  # Data collator
  _UpperCamelCase           =    DataCollatorWithPadding(a__    ,     pad_to_multiple_of=8 ) if training_args.fpaa else None

  # Initialize our Trainer
  _UpperCamelCase           =    Trainer(
      model=a__    ,     args=a__    ,     train_dataset=a__    ,     eval_dataset=a__    ,     compute_metrics=a__    ,     data_collator=a__    ,     )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
  _UpperCamelCase           =    {}
  if training_args.do_eval:
    logger.info("*** Evaluate ***" )

    _UpperCamelCase           =    trainer.evaluate()

    _UpperCamelCase           =    os.path.join(training_args.output_dir    ,     "eval_results.txt" )
    if trainer.is_world_master():
      with open(a__    ,     "w" ) as writer:
        logger.info("***** Eval results *****" )
        for key, value in result.items():
          logger.info("  %s = %s"    ,     a__    ,     a__ )
          writer.write("%s = %s\n" % (key, value) )

        results.update(a__ )

  return results


def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  main()


if __name__ == "__main__":
   main()





def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




